Model performance

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Performance categories

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Upscale model performance

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Very fast models (≤ 1.0s)

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Fast models (1.0s - 3.0s)

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Medium models (3.0s - 5.0s)

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Slow models (> 5.0s)

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Descreen model performance

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Artifact removal model performance

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Daemon mode performance

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Performance improvements

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How daemon mode works

1. Model preloading: Model is loaded into memory once
2. Persistent process: Daemon stays alive between images
3. Fast processing: No model loading overhead per image
4. Automatic cleanup: Daemons close after idle timeout

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Enabling daemon mode

import OpenComicAI from 'opencomic-ai-bin';

// Enable up to 3 concurrent daemons (default)
OpenComicAI.setConcurrentDaemons(3);

// Set idle timeout (default 60 seconds)
OpenComicAI.setDaemonIdleTimeout(60000);

// Process multiple images
for (const image of images) {
  await OpenComicAI.pipeline(image.input, image.output, [
    { model: 'realesrgan-x4plus', scale: 4 }
  ]);
}

// Manually close daemons when done
OpenComicAI.closeAllDaemons();

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Daemon performance details

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Without daemon mode

Processing image 1/10: 6.473s  (includes model loading)
Processing image 2/10: 7.809s  (includes model loading)
Processing image 3/10: 7.690s  (includes model loading)
...
Total: 73.273s

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With daemon mode enabled

Preload model: 1.165s          (one-time cost)
Processing image 1/10: 2.217s  (inference only)
Processing image 2/10: 2.196s  (inference only)
Processing image 3/10: 2.203s  (inference only)
...
Total: 23.199s

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When to use daemon mode

• Batch processing multiple images
• Processing image sequences
• Server applications
• Automated workflows

• Single image processing
• Memory-constrained environments
• Different models for each image

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Optimization strategies

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For speed

1. Choose fast models: Use Very Fast or Fast category models
2. Enable daemon mode: 3-7x speedup for batch processing
3. Use compact models: OpenComic AI Compact variants are optimized for speed
4. Pipeline efficiently: Combine models to avoid multiple read/write cycles

// Efficient: Single pipeline
await OpenComicAI.pipeline('./input.jpg', './output.jpg', [
  { model: 'opencomic-ai-descreen-hard-compact' },
  { model: 'opencomic-ai-artifact-removal-compact' },
  { model: 'realesr-animevideov3', scale: 4 }
]);

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For quality

1. Choose quality models: Accept slower processing for better results
2. Use full-size models: Non-compact variants provide better quality
3. Multi-pass processing: Apply models multiple times for difficult images
4. Combine complementary models: Use specialized models for each task

// Quality-focused pipeline
await OpenComicAI.pipeline('./input.jpg', './output.jpg', [
  { model: '1x_halftone_patch_060000_G' },
  { model: 'opencomic-ai-artifact-removal' },
  { model: 'realesrgan-x4plus', scale: 4 }
]);

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For balanced workflows

1. Mix fast and quality models: Use fast models for preprocessing
2. Scale appropriately: Lower scales process faster
3. Test different models: Find the sweet spot for your content

// Balanced pipeline
await OpenComicAI.pipeline('./input.jpg', './output.jpg', [
  { model: 'opencomic-ai-descreen-hard-compact' },  // Fast
  { model: 'opencomic-ai-artifact-removal-lite' },  // Medium
  { model: 'realcugan', scale: 4, noise: 0 }       // Fast
]);

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Hardware considerations

• GPU: Models run faster with GPU acceleration
• CPU: Multi-core CPUs improve parallel processing
• RAM: Daemon mode requires sufficient memory for loaded models
• Storage: Fast SSD improves file I/O between pipeline stages

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Benchmarking your system

const models = [
  'realesr-animevideov3',
  'realcugan',
  'realesrgan-x4plus-anime'
];

for (const model of models) {
  const start = Date.now();
  await OpenComicAI.pipeline('./test.jpg', './output.jpg', [
    { model, scale: 4 }
  ]);
  console.log(`${model}: ${(Date.now() - start) / 1000}s`);
}

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Related pages

• Upscale models - All upscaling models
• Performance optimization - Optimization techniques
• Daemon configuration - Configure daemon mode